In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from moviepy.editor import VideoFileClip
%matplotlib inline
#%matplotlib qt
Camera Calibration¶
In [2]:
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('./camera_cal/calibration*.jpg')
plt.figure(figsize=(16, 12))
plt.figtext(0.5,0.9,'Images with corners found', fontsize=18, ha='center')
# Step through the list and search for chessboard corners
for i, fname in enumerate(images):
#print(i)
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
plt.subplot(5, 4, len(imgpoints))
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
plt.imshow(img)
plt.title(fname)
plt.axis('off')
#cv2.imshow('img',img)
#cv2.waitKey(500)
plt.show()
#cv2.destroyAllWindows()
#print(objpoints)
#print(imgpoints)
In [3]:
import pickle
%matplotlib inline
# Test undistortion on an image
img = cv2.imread('./camera_cal/calibration1.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
cv2.imwrite('./camera_cal/after_calibration/calibration1_undist.jpg',dst)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "camera_cal/calibration_pickle.p", "wb" ) )
#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=30)
Out[3]:
In [4]:
dist_pickle = pickle.load( open( "./camera_cal/calibration_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
img = cv2.imread('./camera_cal/calibration1.jpg')
nx = 9 # the number of inside corners in x
ny = 5 # the number of inside corners in y
In [5]:
# Define a function that takes an image, number of x and y points,
# camera matrix and distortion coefficients
def corners_unwarp(img, nx, ny, mtx, dist):
# Use the OpenCV undistort() function to remove distortion
undist = cv2.undistort(img, mtx, dist, None, mtx)
# Convert undistorted image to grayscale
gray = cv2.cvtColor(undist, cv2.COLOR_BGR2GRAY)
# Search for corners in the grayscaled image
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
#print('aaaa')
print(ret)
if ret == True:
# If we found corners, draw them! (just for fun)
cv2.drawChessboardCorners(undist, (nx, ny), corners, ret)
# Choose offset from image corners to plot detected corners
# This should be chosen to present the result at the proper aspect ratio
# My choice of 100 pixels is not exact, but close enough for our purpose here
offset = 100 # offset for dst points
# Grab the image shape
img_size = (gray.shape[1], gray.shape[0])
# For source points I'm grabbing the outer four detected corners
src = np.float32([corners[0], corners[nx-1], corners[-1], corners[-nx]])
# For destination points, I'm arbitrarily choosing some points to be
# a nice fit for displaying our warped result
# again, not exact, but close enough for our purposes
dst = np.float32([[offset, offset], [img_size[0]-offset, offset],
[img_size[0]-offset, img_size[1]-offset],
[offset, img_size[1]-offset]])
# Given src and dst points, calculate the perspective transform matrix
M = cv2.getPerspectiveTransform(src, dst)
#print('bbb')
# Warp the image using OpenCV warpPerspective()
warped = cv2.warpPerspective(undist, M, img_size)
# Return the resulting image and matrix
return warped, M
In [6]:
top_down, perspective_M = corners_unwarp(img, nx, ny, mtx, dist)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(top_down)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Image undistortion on one of the test images¶
In [7]:
#test_img = mpimg.imread('./test_images/straight_lines2.jpg')
test_img = mpimg.imread('./test_images/test2.jpg')
test_img_undist = cv2.undistort(test_img, mtx, dist, None, mtx)
r,g,b = cv2.split(test_img_undist) # get b,g,r
test_img_undist_bgr = cv2.merge([b,g,r]) # switch it to rgb. so that the imshow can display correctly
cv2.imwrite('./test_images/test2_undist.jpg',test_img_undist_bgr)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(test_img)
h, w = test_img.shape[0], test_img.shape[1]
ax1.set_ylim([h,0])
ax1.set_xlim([0,w])
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(test_img_undist)
ax2.set_title('Undistorted Image', fontsize=30)
Out[7]:
In [8]:
test_img = mpimg.imread('./test_images/straight_lines2.jpg')
test_img_undist = cv2.undistort(test_img, mtx, dist, None, mtx)
r,g,b = cv2.split(test_img_undist) # get b,g,r
test_img_undist_bgr = cv2.merge([b,g,r]) # switch it to rgb. so that the imshow can display correctly
cv2.imwrite('./test_images/straight_lines2_undist.jpg',test_img_undist_bgr)
h = test_img.shape[0]
w = test_img.shape[1]
#src = np.float32([(602,445), (681,445), (316,650), (1000,650)])
src = np.float32([(581,460), (705,460), (279,675), (1042,675)])
dst = np.float32([(300,0), (w-300,0), (300,h), (w-300,h)])
def unwarp(img, src, dst):
img_size = (img.shape[1], img.shape[0])
M = cv2.getPerspectiveTransform(src, dst)
M_inv = cv2.getPerspectiveTransform(dst, src)
warped = cv2.warpPerspective(img, M, img_size)
return warped, M, M_inv
test_img_undist_warped, M, M_inv = unwarp(test_img_undist, src, dst)
# Visualize unwarp
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(test_img_undist)
x = [src[0][0],src[2][0],src[3][0],src[1][0],src[0][0]]
y = [src[0][1],src[2][1],src[3][1],src[1][1],src[0][1]]
ax1.plot(x, y, color='purple', alpha=0.6, linewidth=4, solid_capstyle='round', zorder=2)
ax1.set_ylim([h,0])
ax1.set_xlim([0,w])
ax1.set_title('Undistorted Image', fontsize=30)
ax2.imshow(test_img_undist_warped)
x2 = [dst[0][0],dst[2][0],dst[3][0],dst[1][0],dst[0][0]]
y2 = [dst[0][1],dst[2][1],dst[3][1],dst[1][1],dst[0][1]]
ax2.plot(x2, y2, color='purple', alpha=0.6, linewidth=4, solid_capstyle='round', zorder=2)
ax2.set_title('Unwarped Image', fontsize=30)
Out[8]:
Define a function that applies Sobel x or y, then takes an absolute value and applies a threshold.¶
In [9]:
# Define a function that applies Sobel x or y,
# then takes an absolute value and applies a threshold.
# Note: calling your function with orient='x', thresh_min=5, thresh_max=100
image = cv2.imread('./test_images/test1.jpg')
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0, 255)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
# Return the result
return binary_output
# Run the function
grad_binary = abs_sobel_thresh(image, orient='x', sobel_kernel=3, thresh=(20, 100))
#grad_binary = abs_sobel_thresh(image, orient='y', sobel_kernel=3, thresh=(0, 40))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
b,g,r = cv2.split(image) # get b,g,r
image = cv2.merge([r,g,b]) # switch it to rgb. so that the imshow can display correctly
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(grad_binary, cmap ='gray' )
ax2.set_title('Sobel Thresholded Gradient', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Define a function to return the magnitude of the gradient for a given sobel kernel size and threshold values¶
In [10]:
# Read in an image
image = mpimg.imread('./test_images/test1.jpg')
# Define a function that applies Sobel x and y,
# then computes the magnitude of the gradient
# and applies a threshold
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
# Return the binary image
return binary_output
# Run the function
mag_binary = mag_thresh(image, sobel_kernel=3, mag_thresh=(40, 80))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(mag_binary, cmap='gray')
ax2.set_title('Sobel Thresholded Magnitude', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Define a function to threshold an image for a given range and Sobel kernel¶
In [11]:
# Read in an image
image = mpimg.imread('./test_images/test1.jpg')
# Define a function that applies Sobel x and y,
# then computes the direction of the gradient
# and applies a threshold.
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# Grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
# Return the binary image
return binary_output
# Run the function
dir_binary = dir_threshold(image, sobel_kernel=15, thresh=(1.0, 1.3))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dir_binary, cmap='gray')
ax2.set_title('Thresholded Grad. Dir.', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Combining and applying each of the thresholding functions defined above¶
In [12]:
image = mpimg.imread('./test_images/test1.jpg')
ksize = 3
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh=(20, 100))
grady = abs_sobel_thresh(image, orient='y', sobel_kernel=ksize, thresh=(0, 20))
mag_binary = mag_thresh(image, sobel_kernel=ksize, mag_thresh=(40, 80))
dir_binary = dir_threshold(image, sobel_kernel=ksize, thresh=(1.0, 1.3))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dir_binary, cmap='gray')
ax2.set_title('Combined Sobel result', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Define a function that thresholds the S-channel of HLS, Use exclusive lower bound (>) and inclusive upper (<=)¶
In [13]:
image = mpimg.imread('./test_images/test1.jpg')
# Define a function that thresholds the S-channel of HLS
# Use exclusive lower bound (>) and inclusive upper (<=)
def hls_select(img, thresh=(0, 255)):
# 1) Convert to HLS color space
# 2) Apply a threshold to the S channel
# 3) Return a binary image of threshold result
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 1
return binary_output
hls_binary = hls_select(image, thresh=(130, 255))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(hls_binary, cmap='gray')
ax2.set_title('Thresholded S', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Define a function that thresholds the red-channel of RGB¶
In [14]:
image = mpimg.imread('./test_images/test1.jpg')
# Define a function that thresholds the red-channel of RGB
def red_select(img, thresh=(0, 255)):
R = img[:,:,0]
G = img[:,:,1]
B = img[:,:,2]
binary = np.zeros_like(R)
binary[(R > thresh[0]) & (R <= thresh[1])] = 1
return binary
red_binary = red_select(image, thresh=(230, 255))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(red_binary, cmap='gray')
ax2.set_title('Thresholded red', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Combine the HSL and Sobel gradient X filters¶
In [15]:
image = mpimg.imread('./test_images/test1.jpg')
ksize = 3
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh=(20, 100))
hls_binary = hls_select(image, thresh=(160, 255))
# Stack each channel to view their individual contributions in green and blue respectively
# This returns a stack of the two binary images, whose components you can see as different colors
color_binary = np.dstack(( np.zeros_like(gradx), gradx, hls_binary)) * 255
# Combine the two binary thresholds
combined_binary = np.zeros_like(gradx)
combined_binary[(hls_binary == 1) | (gradx == 1)] = 1
# Plotting thresholded images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title('Stacked thresholds')
ax1.imshow(color_binary)
ax2.set_title('Combined S channel and gradient thresholds')
ax2.imshow(combined_binary, cmap='gray')
Out[15]:
Combine the Saturation and Red channel filters¶
In [16]:
image = mpimg.imread('./test_images/test1.jpg')
#image = mpimg.imread('./test_images/test2.jpg')
ksize = 3
# Apply each of the thresholding functions
# Used to use (215, 255) (110, 255). The combined binary result is bad
red_binary = red_select(image, thresh=(235, 255))
hls_binary = hls_select(image, thresh=(115, 255))
# Mask the right half as zero (black)
# meaning only use the hls results for the left half of the image
# Therefore, the yellow lane is detected using the combination of both red_binary and hls_binary
# While the white lane is only detected using the red_binary
l_r_midpoint = 660
w = hls_binary.shape[1]
print(hls_binary.shape)
print(w)
hls_binary[:, l_r_midpoint:w] = 0
plt.imshow(hls_binary, cmap='gray')
# Stack each channel to view their individual contributions in green and blue respectively
# This returns a stack of the two binary images, whose components you can see as different colors
color_binary = np.dstack((red_binary, np.zeros_like(red_binary), hls_binary)) * 255
# Combine the two binary thresholds
combined_binary = np.zeros_like(red_binary)
combined_binary[(hls_binary == 1) | (red_binary == 1)] = 1
# Plotting thresholded images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title('Stacked thresholds')
ax1.imshow(color_binary)
ax2.set_title('Combined Saturation channel and Red channel')
ax2.imshow(combined_binary, cmap='gray')
Out[16]:
In [17]:
image = mpimg.imread('./test_images/test1.jpg')
#image = mpimg.imread('./test_images/test2.jpg')
ksize = 3
# Apply each of the thresholding functions
# Used to use (215, 255) (110, 255). The combined binary result is bad
red_binary = red_select(image, thresh=(235, 255))
hls_binary = hls_select(image, thresh=(115, 255))
# Stack each channel to view their individual contributions in green and blue respectively
# This returns a stack of the two binary images, whose components you can see as different colors
color_binary = np.dstack((red_binary, np.zeros_like(red_binary), hls_binary)) * 255
# Combine the two binary thresholds
combined_binary = np.zeros_like(red_binary)
combined_binary[(hls_binary == 1) | (red_binary == 1)] = 1
# Plotting thresholded images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title('Stacked thresholds')
ax1.imshow(color_binary)
ax2.set_title('Combined Saturation channel and Red channel')
ax2.imshow(combined_binary, cmap='gray')
Out[17]:
Do un-distortion and un-warping on the binary image¶
In [18]:
combined_binary_undist = cv2.undistort(combined_binary, mtx, dist, None, mtx)
combined_binary_undist_warped, M, M_inv = unwarp(combined_binary_undist, src, dst)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
ax1.imshow(combined_binary_undist, cmap='gray')
ax1.set_title('Undistorted Binary Image', fontsize=30)
ax1.set_ylim([h,0])
ax1.set_xlim([0,w])
ax2.imshow(combined_binary_undist_warped, cmap='gray')
ax2.set_title('Unwarped Image', fontsize=30)
Out[18]:
Define the polyfit based on the non-zero pixel counting and histogram generation¶
In [19]:
def histogram_polyfit(image):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(image[image.shape[0]//3:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((image, image, image))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 12
# Set height of windows
window_height = np.int(image.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = image.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 40
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
rectangles = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = image.shape[0] - (window+1)*window_height
win_y_high = image.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
rectangles.append((win_y_low, win_y_high, win_xleft_low, win_xleft_high, win_xright_low, win_xright_high))
# Draw the windows on the visualization image
#cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),
#(0,255,0), 2)
#cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),
#(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit, right_fit = (None, None)
# Fit a second order polynomial to each
#left_fit = np.polyfit(lefty, leftx, 2)
#right_fit = np.polyfit(righty, rightx, 2)
if len(leftx) != 0:
left_fit = np.polyfit(lefty, leftx, 2)
if len(rightx) != 0:
right_fit = np.polyfit(righty, rightx, 2)
return left_fit, right_fit, left_lane_inds, right_lane_inds, rectangles
In [20]:
# Generate x and y values for plotting
left_fit, right_fit, left_lane_inds, right_lane_inds, rectangles = histogram_polyfit(combined_binary_undist_warped)
# Create an output image to draw on and visualize the result
out_img = np.uint8(np.dstack((combined_binary_undist_warped, combined_binary_undist_warped, combined_binary_undist_warped))*255)
ploty = np.linspace(0, combined_binary_undist_warped.shape[0]-1, combined_binary_undist_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
for rect in rectangles:
# Draw the windows on the visualization image
cv2.rectangle(out_img,(rect[2],rect[0]),(rect[3],rect[1]),(0,255,0), 2)
cv2.rectangle(out_img,(rect[4],rect[0]),(rect[5],rect[1]),(0,255,0), 2)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = combined_binary_undist_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
Out[20]:
Perform the polyfit based on the calculation of the histogram¶
In [21]:
def last_frame_polyfit(binary_warped, left_fit_prev, right_fit_prev):
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit_prev[0]*(nonzeroy**2) + left_fit_prev[1]*nonzeroy +
left_fit_prev[2] - margin)) & (nonzerox < (left_fit_prev[0]*(nonzeroy**2) +
left_fit_prev[1]*nonzeroy + left_fit_prev[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit_prev[0]*(nonzeroy**2) + right_fit_prev[1]*nonzeroy +
right_fit_prev[2] - margin)) & (nonzerox < (right_fit_prev[0]*(nonzeroy**2) +
right_fit_prev[1]*nonzeroy + right_fit_prev[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
#left_fit = np.polyfit(lefty, leftx, 2)
#right_fit = np.polyfit(righty, rightx, 2)
left_fit, right_fit = (None, None)
if len(leftx) != 0:
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
if len(rightx) != 0:
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
#ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
#left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
#right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fit, right_fit, left_lane_inds, right_lane_inds
Define the function for calcuating the curvature¶
In [22]:
# Define y-value where we want radius of curvature
# I'll choose the maximum y-value, corresponding to the bottom of the image
def curvature(binary_img, left_fit, right_fit, left_lane_inds, right_lane_inds):
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
left_curverad, right_curverad = (0, 0)
ploty = np.linspace(0, binary_img.shape[0]-1, binary_img.shape[0] )
y_eval = np.max(ploty)
#left_fit_cr = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
#right_fit_cr = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_img.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
if len(leftx) != 0 and len(rightx) != 0:
#left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
#right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
# curvation in meters
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
return left_curverad, right_curverad
In [23]:
lef_curv, right_curv = curvature(combined_binary_undist_warped, left_fit, right_fit, left_lane_inds, right_lane_inds)
print('Radius of curvature:', lef_curv, 'm,', right_curv, 'm')
Define the function for calcuating the distance to lane center¶
In [24]:
def center_dist(binary_img, left_fit, right_fit):
center_dist = 0
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
if right_fit is not None and left_fit is not None:
car_position = binary_img.shape[1]/2
l_fit_x_int = left_fit[0]*h**2 + left_fit[1]*h + left_fit[2]
r_fit_x_int = right_fit[0]*h**2 + right_fit[1]*h + right_fit[2]
lane_center_position = (r_fit_x_int + l_fit_x_int) /2
center_dist = (car_position - lane_center_position) * xm_per_pix
return center_dist
In [25]:
center_distance = center_dist(combined_binary_undist_warped, left_fit, right_fit)
print('Distance from lane center:', center_distance, 'm')
Define the function for plotting the results/data back to the original image¶
In [26]:
def draw_results(orig_img, binary_img, left_fit, right_fit, M_inv, curve, center_distance):
if left_fit is None or right_fit is None:
return orig_img
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_img).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
ploty = np.linspace(0, binary_img.shape[0]-1, binary_img.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (M_inv)
newwarp = cv2.warpPerspective(color_warp, M_inv, (image.shape[1], image.shape[0]))
img_copy = np.copy(orig_img)
# Combine the result with the original image
img_with_lane = cv2.addWeighted(img_copy, 1, newwarp, 0.4, 0)
# Add the result text in the image
new_img = np.copy(img_with_lane)
h = img_with_lane.shape[0]
font = cv2.FONT_HERSHEY_DUPLEX
text = 'curve radius: ' + '{:04.2f}'.format(curve) + 'm'
cv2.putText(img_with_lane, text, (10,50), font, 1.5, (255,255,200), 2)
abs_center_dist = abs(center_distance)
text = '{:04.3f}'.format(abs_center_dist) + 'm ' + 'from center'
cv2.putText(img_with_lane, text, (10,100), font, 1.5, (255,255,200), 2)
return img_with_lane
In [27]:
curve = (lef_curv+right_curv)/2
image_with_lanes = draw_results(image, combined_binary_undist_warped, left_fit, right_fit, M_inv, curve, center_distance)
plt.imshow(image_with_lanes)
Out[27]:
Define a class for holding the lane detection related values¶
In [28]:
# Define a class to receive the characteristics of each line detection
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = []
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
def update_fit(self, fit, inds):
# Maintain a group of last 4 fits; Add the fit to the fit group is it exists
if fit is not None:
#print('add fit aaa')
if self.best_fit is not None:
self.diffs = abs(fit-self.best_fit)
if (self.diffs[0] > 0.001 or self.diffs[1] > 1.0 or self.diffs[2] > 100.) and len(self.current_fit) > 0:
self.detected = False
else:
self.detected = True
self.current_fit.append(fit)
if len(self.current_fit) > 4:
# throw out old fits, keep newest n
self.current_fit = self.current_fit[len(self.current_fit)-4:]
self.best_fit = np.average(self.current_fit, axis=0)
# if no fit is found, remove one fit from the group (the oldest one)
else:
#print('add fit bbb')
self.detected = False
if len(self.current_fit) > 0:
# remove oldest fit
self.current_fit = self.current_fit[:len(self.current_fit)-1]
if len(self.current_fit) > 0:
# best_fit is average of the group of fits
self.best_fit = np.average(self.current_fit, axis=0)
Define the function for the overall image processing pipeline¶
In [32]:
def pipeline(img):
ksize = 3
'''
# Apply each of the thresholding functions
# Used to use (215, 255) (110, 255). The combined binary result is bad
#red_binary = red_select(img, thresh=(230, 255))
#hls_binary = hls_select(img, thresh=(130, 255))
red_binary = red_select(img, thresh=(215, 255))
hls_binary = hls_select(img, thresh=(115, 255))
'''
# Apply each of the thresholding functions
# Used to use (215, 255) (110, 255). The combined binary result is bad
red_binary = red_select(img, thresh=(235, 255))
hls_binary = hls_select(img, thresh=(115, 255))
# Mask the right half as zero (black)
# meaning only use the hls results for the left half of the image
# Therefore, the yellow lane is detected using the combination of both red_binary and hls_binary
# While the white lane is only detected using the red_binary
l_r_midpoint = 660
w = hls_binary.shape[1]
hls_binary[:, l_r_midpoint:w] = 0
# Combine the two binary thresholds
combined_binary = np.zeros_like(red_binary)
combined_binary[(hls_binary == 1) | (red_binary == 1)] = 1
'''
h = img.shape[0]
w = img.shape[1]
src = np.float32([(602,445), (681,445), (279,675), (1042,675)])
dst = np.float32([(300,0), (w-300,0), (300,h), (w-300,h)])
# Undistort
img_undistort = cv2.undistort(img, mtx, dist, None, mtx)
#print(img_undistort.shape)
# Perspective Transform
img_undist_warped, M, M_inv = unwarp(img_undistort, src, dst)
'''
# Note that the mtx dist src dst are calculated in the cells at the beginning
# combined_binary_undist = cv2.undistort(combined_binary, mtx, dist, None, mtx)
# combined_binary_undist_warped, M, M_inv = unwarp(combined_binary_undist, src, dst)
combined_binary_warped, M, M_inv = unwarp(combined_binary, src, dst)
return combined_binary_warped, M_inv
#img_bin, M_inv = pipeline(image)
#plt.imshow(img_bin, cmap ='gray')
Define the function for transforming each frame of the video¶
In [33]:
def transform_img(img):
img_copy = np.copy(cv2.undistort(img, mtx, dist, None, mtx))
img_bin = np.zeros_like(img_copy)
img_bin, M_inv = pipeline(img_copy)
# Use histogram_polyfit to re-calculate the fit, if either left or right lines were not detected in last frame
if not l_line.detected or not r_line.detected:
l_fit, r_fit, l_lane_inds, r_lane_inds, _ = histogram_polyfit(img_bin)
# Calculate the fit from scratch using last_frame_polyfit, if both left and right lines were detected
else:
l_fit, r_fit, l_lane_inds, r_lane_inds = last_frame_polyfit(img_bin, l_line.best_fit, r_line.best_fit)
# If the fit jumps too much, do not add that fit
# jumps too much = difference in x-intercepts > 100 px
if l_fit is not None and r_fit is not None:
#print('ccc')
# calculate x-intercept for fits
h = img.shape[0]
l_fit_x_int = l_fit[0]*h**2 + l_fit[1]*h + l_fit[2]
r_fit_x_int = r_fit[0]*h**2 + r_fit[1]*h + r_fit[2]
x_int_diff = abs(r_fit_x_int-l_fit_x_int)
#print(x_int_diff)
if abs(728 - x_int_diff) > 100:
#print('ddd')
l_fit = None
r_fit = None
l_line.update_fit(l_fit, l_lane_inds)
r_line.update_fit(r_fit, r_lane_inds)
# Draw the results/data back to the image
#print(l_line.best_fit)
#print(r_line.best_fit)
if l_line.best_fit is not None and r_line.best_fit is not None:
#print('aaa')
rad_l, rad_r = curvature(img_bin, l_line.best_fit, r_line.best_fit, l_lane_inds, r_lane_inds)
d_center = center_dist(img_bin, l_line.best_fit, r_line.best_fit)
img_out = draw_results(img_copy, img_bin, l_line.best_fit, r_line.best_fit, M_inv, (rad_l+rad_r)/2, d_center)
else:
#print('bbb')
img_out = img_copy
return img_out
In [34]:
l_line = Line()
r_line = Line()
video_output1 = 'project_video_output.mp4'
video_input1 = VideoFileClip('project_video.mp4')#.subclip(24,27)
processed_video = video_input1.fl_image(transform_img)
%time processed_video.write_videofile(video_output1, audio=False)